There are multiple methods to perform spectral clustering [1]}.
We follow the unnormalized spectral clustering approach similar to [2]}, [1]}, [4]}.
The affinity matrix \(A\)  is calculated using the cosine similarity metric.
It is important to prune out the smaller values in \(A\)  to focus more on prominent values in the matrix.
Similar to [1]}, we use the actual similarity values in affinity matrix while calculating the Laplacian matrix.
An unnormalized Laplacian matrix is estimated using the symmetrized \(A\)  as done in [2]}, [1]}.
The Laplacian matrix is subjected to eigendecomposition.
We estimate the number of speakers \(k\)  using the maximum eigengap approach [1]}. Next, we compute the first \(k\)  eigenvectors. The rows of the eigenvector matrix are the \(k\)  dimensional spectral embeddings corresponding to each analyzed speech segment. The estimated spectral embeddings that are expected to be more separable than the original speaker embeddings, are clustered using the standard \(k\) -means algorithm.
